from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
   import datasets




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 @slow
 @require_torch
 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    EncoderDecoderModel.from_encoder_decoder_pretrained("prajjwal1/bert-tiny"							, "prajjwal1/bert-tiny")
   _UpperCamelCase           =    BertTokenizer.from_pretrained("bert-base-uncased")

   _UpperCamelCase           =    bertabert.config.encoder.vocab_size
   _UpperCamelCase           =    tokenizer.sep_token_id
   _UpperCamelCase           =    tokenizer.cls_token_id
   _UpperCamelCase           =    128

   _UpperCamelCase           =    datasets.load_dataset("cnn_dailymail"							, "3.0.0"							, split="train[:1%]")
   _UpperCamelCase           =    datasets.load_dataset("cnn_dailymail"							, "3.0.0"							, split="validation[:1%]")

   _UpperCamelCase           =    train_dataset.select(range(32))
   _UpperCamelCase           =    val_dataset.select(range(16))

   _UpperCamelCase           =    4

   def _map_to_encoder_decoder_inputs(lowercase_				:   Tuple):
     # Tokenizer will automatically set [BOS] <text> [EOS]
     _UpperCamelCase           =    tokenizer(batch["article"]							, padding="max_length"							, truncation=lowercase_							, max_length=512)
     _UpperCamelCase           =    tokenizer(batch["highlights"]							, padding="max_length"							, truncation=lowercase_							, max_length=128)
     _UpperCamelCase           =    inputs.input_ids
     _UpperCamelCase           =    inputs.attention_mask

     _UpperCamelCase           =    outputs.input_ids
     _UpperCamelCase           =    outputs.input_ids.copy()
     _UpperCamelCase           =    [
         [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
     ]
     _UpperCamelCase           =    outputs.attention_mask

     assert all(len(lowercase_) == 512 for x in inputs.input_ids)
     assert all(len(lowercase_) == 128 for x in outputs.input_ids)

     return batch

   def _compute_metrics(lowercase_				:   Optional[Any]):
     _UpperCamelCase           =    pred.label_ids
     _UpperCamelCase           =    pred.predictions

     # all unnecessary tokens are removed
     _UpperCamelCase           =    tokenizer.batch_decode(lowercase_							, skip_special_tokens=lowercase_)
     _UpperCamelCase           =    tokenizer.batch_decode(lowercase_							, skip_special_tokens=lowercase_)

     _UpperCamelCase           =    sum([int(pred_str[i] == label_str[i]) for i in range(len(lowercase_))]) / len(lowercase_)

     return {"accuracy": accuracy}

   # map train dataset
   _UpperCamelCase           =    train_dataset.map(
       _map_to_encoder_decoder_inputs							, batched=lowercase_							, batch_size=lowercase_							, remove_columns=["article", "highlights"]							, )
   train_dataset.set_format(
       type="torch"							, columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							, )

   # same for validation dataset
   _UpperCamelCase           =    val_dataset.map(
       _map_to_encoder_decoder_inputs							, batched=lowercase_							, batch_size=lowercase_							, remove_columns=["article", "highlights"]							, )
   val_dataset.set_format(
       type="torch"							, columns=["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask", "labels"]							, )

   _UpperCamelCase           =    self.get_auto_remove_tmp_dir()

   _UpperCamelCase           =    SeqaSeqTrainingArguments(
       output_dir=lowercase_							, per_device_train_batch_size=lowercase_							, per_device_eval_batch_size=lowercase_							, predict_with_generate=lowercase_							, evaluation_strategy="steps"							, do_train=lowercase_							, do_eval=lowercase_							, warmup_steps=0							, eval_steps=2							, logging_steps=2							, )

   # instantiate trainer
   _UpperCamelCase           =    SeqaSeqTrainer(
       model=lowercase_							, args=lowercase_							, compute_metrics=_compute_metrics							, train_dataset=lowercase_							, eval_dataset=lowercase_							, tokenizer=lowercase_							, )

   # start training
   trainer.train()





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					['''DebertaTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DebertaForMaskedLM''',
       '''DebertaForQuestionAnswering''',
       '''DebertaForSequenceClassification''',
       '''DebertaForTokenClassification''',
       '''DebertaModel''',
       '''DebertaPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFDebertaForMaskedLM''',
       '''TFDebertaForQuestionAnswering''',
       '''TFDebertaForSequenceClassification''',
       '''TFDebertaForTokenClassification''',
       '''TFDebertaModel''',
       '''TFDebertaPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
   from .tokenization_deberta import DebertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_fast import DebertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta import (
          DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaForMaskedLM,
          DebertaForQuestionAnswering,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaModel,
          DebertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta import (
          TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
          TFDebertaPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




